Apparatus and methods for automated control of an electric vertical takeoff and landing aircraft

ABSTRACT

An apparatus for automated ground control of an electric vertical takeoff and landing aircraft includes a flight controller and a remote device. Remote device may transmit a location datum to a flight controller. Location datum may store a location of a charger, terminal, airport, or the like. Flight controller may authorize the location datum and direct aircraft using the information from the location datum.

FIELD OF THE INVENTION

The present invention generally relates to the field of aircraft. Inparticular, the present invention is directed to apparatus and methodsfor automated control of an electric vertical takeoff and landingaircraft.

BACKGROUND

In an electric vertical takeoff and landing aircraft (eVTOL), itdesirable to have an automated ground control apparatus to help guide aneVTOL to a desired location. Currently, there is a need for an automatedground control apparatus for eVTOLs.

SUMMARY OF THE DISCLOSURE

In an aspect an apparatus for automated ground control of an electricvertical takeoff and landing aircraft is shown. The apparatus includes:at least a flight controller incorporated in a grounded electricaircraft, the at least a flight controller configured to: receive alocation datum from a remote device wherein the location datum comprisesat least a location of a charger; determine an authority status of thelocation datum; generate a command datum as a function of the locationdatum and the authority status, wherein the authority status comprisesthe validation of a location datum; and initiate an operation of amaneuver component of the aircraft as a function of the command datumand the authority status.

In another aspect a method for automated ground control of an electricvertical takeoff and landing aircraft includes: receiving, from a remotedevice, a location datum wherein the location datum comprises at least alocation of a charger; determining, at the flight controller, anauthority status of the location datum; generating, at the flightcontroller, a command datum as a function of the location datum and theauthority status, wherein the authority status comprises the validationof a location datum; and initiating, at the flight controller, anoperation of a maneuver component of the electric vertical takeoff andlanding aircraft as a function of the command datum and the authoritystatus.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of an apparatus forautomated ground control in an electric vertical takeoff and landingaircraft;

FIG. 2 is an illustrative flow diagram for an exemplary embodiment of amethod for automated ground control in an electrical vertical takeoffand landing aircraft;

FIG. 3 is an illustrative diagram of a flight controller;

FIG. 4 is an exemplary diagram of a machine-learning model;

FIG. 5 is an exemplary representation of an electric vertical takeoffand landing aircraft; and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatus and method for automated ground control of an electricvertical takeoff and landing aircraft. In an embodiment, autopilot isonly active in zones controlled by air traffic control. Automated groundcontrol may assist users to get aircrafts into proper locations.

Aspects of the present disclosure may include a remote device totransmit location data to a flight controller. Controller may authorizethe information in the location data.

Referring now to FIG. 1 , an exemplary embodiment of an apparatus 100for automated ground control of an electrical vertical takeoff andlanding aircraft is illustrated in accordance with one or moreembodiments of the present disclosure. In one or more embodiments,apparatus 100 includes a flight controller 104. Flight controller 104may include any computing device as described in this disclosure,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described inthis disclosure. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. Flight controller 104 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. Flight controller 104 may interfaceor communicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting flight controller 104 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus, or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Flightcontroller 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Flight controller 104 may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. Flight controller 104 may distribute oneor more computing tasks as described below across a plurality ofcomputing devices of computing device, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices. Flight controller 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofapparatus 100 and/or a computing device.

With continued reference to FIG. 1 , flight controller 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, flightcontroller 104 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Flight controller 104 may perform any step or sequence of stepsas described in this disclosure in parallel, such as simultaneouslyand/or substantially simultaneously performing a step two or more timesusing two or more parallel threads, processor cores, or the like;division of tasks between parallel threads and/or processes may beperformed according to any protocol suitable for division of tasksbetween iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 1 , apparatus 100 includes a remote computingdevice 108. Remote computing device 108 may include a computing deviceor plurality of computing devices consistent with the entirety of thisdisclosure. Remote computing device 108 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, remote computing device 108may be configured to perform a single step or sequence repeatedly untila desired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Remote computing device108 may perform any step or sequence of steps as described in thisdisclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

Continuing to refer to FIG. 1 , flight controller 104 is configured toreceive a location datum 116 from remote device 108. Flight controlleris incorporated in a grounded electric aircraft. As used herein, a“location datum” refers to any element of data identifying a particularplace. Location datum 116 may refer to the geographic coordinates of aplace. A location datum 116 may include GPS coordinates, longitude,latitude, elevation of a particular location, and the like. In anembodiment, a location datum 116 may include data about a charger,storage hanger, terminal, airport, or the like. A charger may includewired chargers, wireless chargers, charger pads, or the like. A chargermay use a horizontal cable arrangement as discussed in U.S. patentapplication Ser. No. 17/736,574, filed May 4, 2022, and titled “METHODSAND SYSTEMS FOR CHARGING AN ELECTRIC AIRCRAFT INCLUDING A HORIZONTALCABLE ARRANGEMENT”. Remote device 108 may be located at the location ofthe location datum 116. In an embodiment, remote device 108 may belocated within a charger, at an airport terminal, at a storage hanger,and the like. In such instances, remote device 108 may transmit alocation datum 116 of the location of the remote device 108. Remotedevice 108 may include a GPS sensor, and the like to detect a locationdatum 116. Apparatus 100 may be configured to operate outside of airtraffic control purview, such that the location datums received may beoutside of air traffic control purview. In an embodiment, apparatus 100may only operate in a grounded electric aircraft. As used herein, a“grounded electric aircraft” is an aircraft that is on the ground andnot flying. In an embodiment, apparatus 100 may not get a location datum116 while eVTOL aircraft 500 (also referred to as “aircraft”) istaxiing. Additionally or alternatively, flight controller 104 may beconfigured to generate the location datum 116 as a function of userinput. In an embodiment, a user may input a location datum 116 on agraphical user interface found on a display. Displays are discussed infurther detail below. Location datum 116 may indicate an exact location,such as coordinates found using a GPS. Additionally, or alternatively,location datum 116 may indicate a relative location, i.e. a locationrelative to the aircraft. It may include a heading and/or distance tothe location datum from the aircraft's current location.

With continued reference to FIG. 1 , flight controller 104 may becommunicatively connected to remote device 108 and may be configured toreceive location datum 116 and control datum from remote device 108.“Communicatively connected”, for the purposes of this disclosure, is aprocess whereby one device, component, or circuit is able to receivedata from and/or transmit data to another device, component, or circuit;communicative connection may be performed by wired or wirelesselectronic communication, either directly or by way of one or moreintervening devices or components. In an embodiment, communicativeconnection includes electrically connection an output of one device,component, or circuit to an input of another device, component, orcircuit. Communicative connection may be performed via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connection may include indirect connections via “wireless”connection, low power wide area network, radio communication, opticalcommunication, magnetic, capacitive, or optical connection, or the like.In one or more embodiments, flight controller 104 is configured togenerate command datum 120 as a function of location datum 116 and anauthority status 136. “Command datum” may include any data describing anadjustment to and/or operation of at least a maneuver component in anaircraft 500. Command datum 120 may indicate a command to change theheading or trim of an aircraft 500. Command datum 120 may furtherinclude a command to adjust the torque produced by a propulsor in anaircraft 500. Command datum 120 may indicate a command to change anaircraft's yaw. “Yaw”, for the purposes of this disclosure, refers to anaircraft's turn angle, when an aircraft rotates about an imaginaryvertical axis intersecting the center of the earth and the fuselage ofthe aircraft 500. “Throttle”, for the purposes of this disclosure,refers to an aircraft outputting an amount of thrust from a propulsor.Throttle may refer to a desire to increase or decrease thrust producedby at least a propulsor. Command datum 120 may be a command that iswithin ground control limits set by a user, such as a maximum speed,minimum altitude, or the like. Command datum 120 may be generated basedon an authority status of a location, for example, by a maneuver controlalgorithm. Command datum 120 may include a desired torque, wherein theat least a maneuver component may be configured to operate at thedesired torque. Command datum 120 may also include at least an elementof data identifying at least a command, at least a maneuver, at least anelement of the maneuver control algorithm, and the like. In one or moreembodiments maneuver component may initiate an operation of maneuvercomponent of aircraft 500 as a function of the command datum and theauthority status. An operation of a maneuver component may include anactuator 128 moving maneuver component as a function of the commanddatum and authority status. As used herein, a “maneuver component” is acomponent on an aircraft capable of initiating movement of the aircraft.For example, and without limitation, an operation of a maneuvercomponent may include an initiating or terminating an operation of apusher component of an aircraft. More specifically, a flight controller104 may be configured to initiate operation of the pusher componentwhich, in an embodiment, includes initiating rotation of the pushercomponent such that the rotation of the pusher component generatesforward or substantially horizontal thrust. More specifically, a flightcontroller 104 may be configured to control the brake calipers such thatan aircraft may slow down or change directions. In an embodiment, when apropulsor twists and pulls air behind it, it will, at the same time,push an aircraft forward with an equal amount of force. System 100 mayoperate propulsor and braking systems at the same time to move theaircraft 500 forward and adjust the yaw of aircraft 500 at the sametime. For example, controller 104 may selectively engage the brakes onthe wheels of aircraft 500, such that only one of the two wheels areengaged. This may steer the aircraft left or right. While braking isengaged, controller 104 may activate the propulsors to allow forhorizontal movement of the aircraft. The combination of the propulsorsand brakes may allow the aircraft to maneuver to the location of thelocation datum 116. Additional information on aircraft braking can befound in U.S. patent application Ser. No. 17/732,134, filed Apr. 28,2022, and titled “SYSTEMS AND METHODS FOR AN ELECTRIC VERTICAL TAKEOFFAND LANDING AIRCRAFT BRAKING SYSTEM”, the entirety of which isincorporated herein by reference.

In one or more embodiments, an “authority status” is a datum assigned toa location datum 116 that relates to whether an aircraft is authorizedto use automated ground control to go to a location. In one or moreembodiments, an authority status may be represented using a color codeof green, yellow, and red, wherein “green” may represent full approvalfrom the flight controller 104 for the automated ground controlapparatus to go to a location. “Yellow” may represent a ‘furtherapproval needed status’ wherein a user may need to provide the flightcontroller 104 more information on the authority status. In anembodiment, a user may need to manually provide authorization for alocation datum 116. “Red” may represent an unauthorized location datum116. In an embodiment, flight controller 104 may compare the currentlocation datum 116 of an aircraft with the location datum 116 beingtransmitted and determine that a location datum 116 is unauthorized. Forexample, a flight controller 104 may deny authorization of a locationdatum 116 of a charger located 300 miles away from the present locationof an aircraft. Flight controller 104 may determine that a locationdatum 116 is not authorized by user input, wherein a user may input aradius of where an aircraft may use the automated ground control. A usermay override or modify an authority status provided by a flightcontroller 104. In an embodiment, a user may decide that a locationdatum 116 is authorized. In another embodiment, a user may decide toenter a different location datum 116. Authority status may berepresented in ways other than colors that represent three categories.The categories may include location datum 116 that is authorized, needuser approval, or not authorized.

In one or more embodiments, flight controller 104 may implement amaneuver control algorithm to determine an authority status of alocation datum 116. For the purposes of this disclosure, a “maneuvercontrol algorithm” is an algorithm that sets associates an authoritystatus with a corresponding location datum 116. Maneuver controlalgorithm may include machine-learning processes that are used tocalculate a set of authority statuses and command datum 120.Machine-learning process may be trained by using training dataassociated with past calculations for the aircraft, data related to pastcalculations in other aircrafts, calculations performed based onsimulated data, or any other training data described in this disclosure.Training data may include previous ranges an aircraft can travel giventhe amount of charge left. In this instance, training data may providean acceptable radius an aircraft may travel to a charging station. Usingthis training data, an aircraft may not authorize a location datum of acharging station outside the aircraft's acceptable radius. Authoritystatus may include control thresholds, where performing commands withinof a predetermined range result in full authorization of a locationdatum 116, user approval of a location datum 116, or no authorization ofa location datum 116. For example, and without limitation, if athreshold for full authorization may be a given radius an aircraft cantravel to. Full authorization may also include machine-learning processfactoring in the areas under air traffic control. In an embodiment, if alocation datum 116 sent to the flight controller 104 is on a tarmac,location datum 116 may not be authorized. In another embodiment, if alocation datum 116 is in a range slightly outside the given authorizedradius, user may decide to approve or not approve of a location datum116.

Alternatively or additionally, and with continued reference to FIG. 1 ,system 100 may include actuator 128 which is communicatively connectedto flight controller 104. In an embodiment, actuator 128 may becommunicatively connected to the input control. Actuator 128 isconfigured to receive command datum 120 from flight controller 104. Inan embodiment, flight controller 104 and/or user may determine anauthority status and then flight controller 104 may then transmitcommand datum 120 according to a determined authority status 136 of thelocation datum 116. Command datum 120 may then be transmitted toactuator 128, which in turn moves a maneuver component of aircraft 132so that aircraft 132 may move towards a location.

Still referring to FIG. 1 , in an embodiment, actuator 128 may include acomputing device or plurality of computing devices consistent with theentirety of this disclosure. Actuator 128 may be designed and/orconfigured to perform any method, method step, or sequence of methodsteps in any embodiment described in this disclosure, in any order andwith any degree of repetition. For instance, flight actuator 128 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Actuator 128 may performany step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Continuing to refer to FIG. 1 , actuator 128 may include a piston andcylinder system configured to utilize hydraulic pressure to extend andretract a piston connected to at least a portion of aircraft. Actuator128 may include a stepper motor or server motor configured to utilizeelectrical energy into electromagnetic movement of a rotor in a stator.Actuator 128 may include a system of gears connected to an electricmotor configured to convert electrical energy into kinetic energy andmechanical movement through a system of gears. Actuator 128 may includeone or more inverters capable of driving one or more propulsorsconsistent with the entirety of this disclosure utilizing the hereindisclosed system. Actuator 128 may include brake calipers capable ofslowing down or changing directions of an aircraft. Actuator 128, one ofthe combination of components thereof, or another component configuredto receive data from flight controller 104 and remote device 108, ifloss of communication is detected, may be configured to implement areduced function controller. The reduced function controller may reactdirectly to remote device 108, or other raw data inputs, as described inthe entirety of this disclosure. Actuator 128 may include components,processors, computing devices, or the like configured to detect, as afunction of time, loss of communication with flight controller 104.

Continuing to refer to FIG. 1 , actuator 128 may be further configuredto command a maneuver component as a function of command datum 120. Inone or more embodiments, a maneuver component may include a propulsorand/or control surface. In some embodiments commanding at least amaneuver component includes changing a movement and/or attitude ofaircraft 132. A movement and/or attitude change of an aircraft mayinclude a change in an aircraft's yaw, throttle, torque, heading, trim,or any change that causes the aircraft to perform a movement. Actuator128 may be configured to move control surfaces of the aircraft in one orboth of its two main modes of locomotion or adjust thrust produced atany of the propulsors. These electronic signals can be translated toaircraft control surfaces. These control surfaces, in conjunction withforces induced by environment and propulsion systems, are configured tomove the aircraft through a fluid medium, an example of which is air. A“control surface” as described herein, is any form of a mechanicallinkage with a surface area that interacts with forces to move anaircraft. A control surface may include, as a non-limiting example,brake pads, ailerons, flaps, leading edge flaps, rudders, elevators,spoilers, slats, blades, stabilizers, stabilators, airfoils, acombination thereof, or any other mechanical surface are used to controlan aircraft in a fluid medium. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousmechanical linkages that may be used as a control surface, as used, anddescribed in this disclosure.

In an embodiment, actuator 128 may be mechanically connected to acontrol surface at a first end and mechanically connected to an aircraftat a second end. As used herein, a person of ordinary skill in the artwould understand “mechanically connected” to mean that at least aportion of a device, component, or circuit is connected to at least aportion of the aircraft via a mechanical connection. Said mechanicalconnection can include, for example, rigid connection, such as beamconnection, bellows connection, bushed pin connection, constantvelocity, split-muff connection, diaphragm connection, disc connection,donut connection, elastic connection, flexible connection, fluidconnection, gear connection, grid connection, hirth joints, hydrodynamicconnection, jaw connection, magnetic connection, Oldham connection,sleeve connection, tapered shaft lock, twin spring connection, rag jointconnection, universal joints, or any combination thereof. In anembodiment, mechanical connection can be used to connect the ends ofadjacent parts and/or objects of an aircraft. Further, in an embodiment,mechanical connection can be used to join two pieces of rotatingaircraft components. Control surfaces may each include any portion of anaircraft that can be moved or adjusted to affect altitude, airspeedvelocity, groundspeed velocity or direction during ground movements. Forexample, control surfaces may include a component used to affect theaircrafts' roll and pitch which may comprise one or more ailerons,defined herein as hinged surfaces which form part of the trailing edgeof each wing in a fixed wing aircraft, and which may be moved viamechanical means such as without limitation servomotors, mechanicallinkages, or the like, to name a few. As a further example, controlsurfaces may include a rudder, which may include, without limitation, asegmented rudder. The rudder may function, without limitation, tocontrol yaw of an aircraft. Additionally, a control surface may includebrake pads, which may function to control the forward velocity or yaw ofan aircraft. Also, control surfaces may include other flight controlsurfaces such as propulsors, rotating flight controls, or any otherstructural features which can adjust the movement of the aircraft.

With continued reference to FIG. 1 , at least a portion of an aircraftmay include at least a maneuver component. At least a maneuver componentmay include any component of an aircraft. In an embodiment, at least amaneuver component may include a propulsor and the propulsor may includea propeller, a blade, or any combination of the two. A “propulsor”, asused herein, is a component or device used to propel a craft by exertingforce on a fluid medium, which may include a gaseous medium such as airor a liquid medium such as water. In an embodiment, when a propulsortwists and pulls air behind it, it will, at the same time, push anaircraft forward with an equal amount of force. The more air pulledbehind an aircraft, the greater the force with which the aircraft ispushed forward. Propulsor may include any device or component thatconsumes electrical power on demand to propel an aircraft in a directionor other vehicle while on ground or in-flight. The function of apropeller is to convert rotary motion from an engine or other powersource into a swirling slipstream which pushes the propeller forwards orbackwards. The propulsor may include a rotating power-driven hub, towhich are attached several radial airfoil-section blades such that thewhole assembly rotates about a longitudinal axis. The blade pitch of thepropellers may, for example, be fixed, manually variable to a few setpositions, automatically variable (e.g. a “constant-speed” type), or anycombination thereof. In an embodiment, propellers for an aircraft aredesigned to be fixed to their hub at an angle similar to the thread on ascrew makes an angle to the shaft; this angle may be referred to as apitch or pitch angle which will determine the speed of the forwardmovement as the blade rotates.

In an embodiment, a propulsor can include a thrust element which may beintegrated into the propulsor. The thrust element may include, withoutlimitation, a device using moving or rotating foils, such as one or morerotors, an airscrew or propeller, a set of airscrews or propellers suchas contra-rotating propellers, a moving or flapping wing, or the like.Further, a thrust element, for example, can include without limitation amarine propeller or screw, an impeller, a turbine, a pump-jet, a paddleor paddle-based device, or the like.

Still referring to FIG. 1 , remote device 108 is communicativelyconnected to flight controller 104, where flight controller 104 isconfigured to receive location datum 116 from remote device 108 andcommand datum 120 for a user to view, such as via a display 124.Computing device may be configured to display information to a user.“Displaying” may include information in graphical form, tactile feedbackform, audio form, or any other method of delivering information to auser. In an embodiment, remote device 108 may display command datum 120and/or authority status 136 to the user. In embodiments, remote device108 may include multiple devices, where each device is configured todisplay command datum 120. In other embodiments, computing device mayinclude multiple devices where some of the devices may displayinformation that is different than information displayed in the otherdevices. In an embodiment, computing device may include a graphical userinterface (GUI) incorporated in the aircraft. As described herein, agraphical user interface is a form of user interface that allows usersto interact with flight controller 104 through graphical icons and/orvisual indicators. The user may, without limitation, interact withgraphical user interface through direct manipulation of the graphicalelements. Graphical user interface may be configured to display at leastan element of a flight plan, as described in detail below. As anexample, and without limitation, graphical user interface may bedisplayed on any electronic device, as described herein, such as,without limitation, a computer, tablet, and/or any other visual displaydevice. Display 124 is configured to present, to a user, informationrelated to the flight plan. Display 124 may include a graphical userinterface, multi-function display (MFD), primary display, gauges,graphs, audio cues, visual cues, information on a heads-up display (HUD)or a combination thereof. Display 124 may be disposed in a projection,hologram, or screen within a user's helmet, eyeglasses, contact lens, ora combination thereof. Remote device 108 may display the command datum120 and/or authority status 136 in graphical form. Graphical form mayinclude a two-dimensional plot of two variables that represent datareceived by the controller, such as the control datum and the relatedauthority status. In one embodiment, computing device may also displaythe user's input in real-time. In embodiments, computing device mayrelay the command datum 120 in audio form.

Now referring to FIG. 2 , an exemplary depiction of a method 200 forautomated ground control of an electrical vertical takeoff and landingaircraft. Step 205 of method 200 includes receiving, from a remotedevice 108, a location datum 116 wherein the location datum 116 includesat least a location of a charger. A charger may be a wireless charger ora corded charger. In the case of a wireless charger, a location datum116 may assist aircraft in finding the optimal charging location.Wireless chargers may have an optimal position for charging. In anembodiment, aircraft may charge best directly over the center of awireless charger. Automated ground control of an aircraft may make smallor large corrections an aircraft's position to find optimal charging. Inanother embodiment, location datum 116 may also include adjustments inorientation of an aircraft. As used herein, “orientation” refers to theposition of an object on a plane. For example, location datum 116 maymake small adjustments in orientation of an aircraft that do not resultin changes in longitude or latitude.

Step 210 of method 200 includes determining, at the flight controller104, an authority status of the location datum 116. Flight controller104 may use machine learning to determine the authority status of alocation datum 116. In an embodiment, an authorized location datum 116may activate command datum 120 such that the aircraft may move locationsor positions.

Step 215 of method 200 includes generating, at the flight controller104, a command datum 120 as a function of the location datum 116 and theauthority status, wherein the authority status includes the validationof a location datum 116. A command datum 120 may activate actuators onaircraft such that there is mechanical movement.

Step 220 of method 200 includes initiating, at the flight controller104, an operation of a maneuver component of the aircraft as a functionof the command datum 120 and the authority status. In an embodiment,initiating an operation of a maneuver component may allow the aircraftto move to a location given in the flight datum.

Now referring to FIG. 3 , an exemplary embodiment 300 of a flightcontroller 304 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 304 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 304may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 304 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a signal transformation component 308. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 308 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component308 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 308 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 308 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 308 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 3 , signal transformation component 308 may beconfigured to optimize an intermediate representation 312. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 308 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 308 may optimizeintermediate representation 312 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 308 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 308 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 304. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 308 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include a reconfigurable hardware platform 316. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 316 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 3 , reconfigurable hardware platform 316 mayinclude a logic component 320. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 320 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 320 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 320 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 320 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 320 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 312. Logiccomponent 320 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 304. Logiccomponent 320 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 320 may beconfigured to execute the instruction on intermediate representation 312and/or output language. For example, and without limitation, logiccomponent 320 may be configured to execute an addition operation onintermediate representation 312 and/or output language.

In an embodiment, and without limitation, logic component 320 may beconfigured to calculate a flight element 324. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 324 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 324 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 324 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 3 , flight controller 304 may include a chipsetcomponent 328. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 328 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 320 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 328 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 320 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 328 maymanage data flow between logic component 320, memory cache, and amaneuver component 332. As used in this disclosure a “maneuvercomponent” is a portion of an aircraft that can be moved or adjusted toaffect one or more flight elements. For example, maneuver component 332may include a component used to affect the aircrafts' roll and pitchwhich may comprise one or more ailerons. As a further example, maneuvercomponent 332 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 328 may be configured to communicate witha plurality of maneuver components as a function of flight element 324.For example, and without limitation, chipset component 328 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 3 , flight controller 304may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 304 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 324. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 304 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 304 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 3 , flight controller 304may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 324 and a pilot signal336 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 336may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 336 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 336may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 336 may include an explicitsignal directing flight controller 304 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 336 may include an implicit signal, wherein flight controller 304detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 336 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 336 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 336 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 336 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal336 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 3 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 304 and/or a remote device 108 may or may not usein the generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 304.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 3 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 304 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 3 , flight controller 304 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor, and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 304. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 304 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 304 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 3 , flight controller 304 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus, or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 3 , flight controller 304may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller304 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 304 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 304 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the maneuver component ofthe plurality of maneuver components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Massachusetts, USA. In an embodiment, and withoutlimitation, control algorithm may be configured to generate anauto-code, wherein an “auto-code,” is used herein, is a code and/oralgorithm that is generated as a function of the one or more modelsand/or software's. In another embodiment, control algorithm may beconfigured to produce a segmented control algorithm. As used in thisdisclosure a “segmented control algorithm” is control algorithm that hasbeen separated and/or parsed into discrete sections. For example, andwithout limitation, segmented control algorithm may parse controlalgorithm into two or more segments, wherein each segment of controlalgorithm may be performed by one or more flight controllers operatingon distinct maneuver components.

In an embodiment, and still referring to FIG. 3 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of maneuver component 332. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata maneuver component is malfunctioning and/or in a failure state.

Still referring to FIG. 3 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 304. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 312 and/or output language from logiccomponent 320, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 3 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 3 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 3 , flight controller 304 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 304 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of maneuver components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 3 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 3 , flight controller may include asub-controller 340. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 304 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 340may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 340 may include any component of any flightcontroller as described above. Sub-controller 340 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 340may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 340 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or maneuver components.

Still referring to FIG. 3 , flight controller may include aco-controller 344. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 304 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 344 mayinclude one or more controllers and/or components that are similar toflight controller 304. As a further non-limiting example, co-controller344 may include any controller and/or component that joins flightcontroller 304 to distributer flight controller. As a furthernon-limiting example, co-controller 344 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 304 to distributed flight control system. Co-controller 344may include any component of any flight controller as described above.Co-controller 344 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 3 , flightcontroller 304 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 304 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 408 given data provided as inputs 412;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 400 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 404. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process428 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 5 , an embodiment of an electric vertical takeoffand landing aircraft 500 is presented. As used herein, a verticaltake-off and landing (eVTOL) aircraft is one that can hover, take off,and land vertically. An eVTOL, as used herein, is an electricallypowered aircraft typically using an energy source, of a plurality ofenergy sources to power the aircraft. In order to optimize the power andenergy necessary to propel the aircraft. eVTOL may be capable ofrotor-based cruising flight, rotor-based takeoff, rotor-based landing,fixed-wing cruising flight, airplane-style takeoff, airplane-stylelanding, and/or any combination thereof. Rotor-based flight, asdescribed herein, is where the aircraft generated lift and propulsion byway of one or more powered rotors connected with an engine, such as a“quad copter,” multi-rotor helicopter, or other vehicle that maintainsits lift primarily using downward thrusting propulsors. Fixed-wingflight, as described herein, is where the aircraft is capable of flightusing wings and/or foils that generate life caused by the aircraft'sforward airspeed and the shape of the wings and/or foils, such asairplane-style flight. Aircraft 500 may contain a flight controller 104,landing gear assembly, rudders, and the like. Landing gear assembly maybe located at the base of the aircraft. Landing gear assembly mayfunction as a component of an undercarriage of an aircraft that supportsthe weight of the aircraft when it is not in the air. Landing gearassembly may house wheels and brakes for wheels that control movement ofan aircraft. Landing gear assembly may be composed of any materialsuitable for composition of an aircraft as described above, includingwithout limitation wood, fabric, aluminum, steel, titanium, polymers,carbon fiber, graphite-epoxy, epoxy fiber glass, fiber glass, metalalloys, epoxy resin, resin, composites, and the like. Landing gearassembly may be designed with a consideration of energy absorptionduring a landing or crash landing. Aircraft 300 may also contain one ormore rudders. Rudder may be located on the rear wings of the aircraft.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. An apparatus for automated ground control of an electric verticaltakeoff and landing aircraft (eVTOL), the apparatus comprising: at leasta flight controller incorporated in the grounded eVTOL, the at least aflight controller configured to: receive a first location datum from aremote device wherein the first location datum comprises at least alocation of a charger; determine an authority status of the firstlocation datum, wherein the flight control implements a maneuver controlalgorithm to determine the authority status of the first location datum,wherein a location datum of the eVTOL is compared with the firstlocation datum being transmitted and a plurality of control thresholds,each control threshold associated with one of a plurality of authoritystatuses in order to authorize a utilization of an automated groundcontrol, wherein the authority status is represented by colorindicators, wherein the authority status is represented in graphicalform, and wherein a user can override an authority status provided bythe flight controller; generate a command datum as a function of thefirst location datum and the authority status, wherein the authoritystatus comprises the validation of the first location datum; andinitiate an operation of a maneuver component of the eVTOL as a functionof the command datum and the authority status.
 2. The apparatus of claim1, wherein the flight controller is configured to generate the locationdatum of the eVTOL as a function of user input.
 3. The apparatus ofclaim 1, wherein the charger comprises a wireless charger.
 4. Theapparatus of claim 1, wherein the first location datum comprises alocation of a storage hanger.
 5. The apparatus of claim 1, furthercomprising an actuator connected to the flight controller, wherein theactuator is configured to: receive the command datum from the flightcontroller; and move the maneuver component as a function of the commanddatum and the authority status.
 6. The apparatus of claim 5, wherein theactuator is configured to convert the command datum into mechanicalmovement of a maneuver component of the eVTOL.
 7. The apparatus of claim5, wherein the actuator comprises brake calipers.
 8. The apparatus ofclaim 1, wherein the automated ground control is configured to operateoutside of air traffic control purview.
 9. The apparatus of claim 1,wherein the remote device is configured to be located at the location ofthe first location datum.
 10. The apparatus of claim 1, wherein theauthority status is configured to be modified by a user.
 11. A methodfor automated ground control of an electric vertical takeoff and landingaircraft (eVTOL), the method comprising: receiving, from a remotedevice, a first location datum wherein the first location datumcomprises at least a location of a charger; determining, at the flightcontroller, an authority status of the first location datum, wherein theflight control implements a maneuver control algorithm to determine theauthority status of the first location datum, and wherein a locationdatum of the eVTOL is compared with the first location datum beingtransmitted and a plurality of control thresholds, each controlthreshold associated with one of a plurality of authority statuses inorder to authorize a utilization of an automated ground control, whereinthe authority status is represented by color indicators, wherein theauthority status is represented in graphical form, and wherein a usercan override an authority status provided by the flight controller;generating, at the flight controller, a command datum as a function ofthe first location datum and the authority status, wherein the authoritystatus comprises the validation of the first location datum; andinitiating, at the flight controller, an operation of a maneuvercomponent of the eVTOL as a function of the command datum and theauthority status.
 12. The method of claim 11, wherein the flightcontroller is configured to generate the location datum of the eVTOL asa function of user input.
 13. The method of claim 11, wherein thecharger comprises a wireless charger.
 14. The method of claim 11,wherein the first location datum comprises a location of a storagehanger.
 15. The method of claim 11, further comprising an actuatorconnected to the flight controller, wherein the actuator is configuredto: receive the command datum from the flight controller; and move themaneuver component as a function of the command datum and the authoritystatus.
 16. The method of claim 15, wherein the actuator is configuredto convert the command datum into mechanical movement of a maneuvercomponent of the eVTOL.
 17. The method of claim 15, wherein the actuatorcomprises brake calipers.
 18. The method of claim 11, wherein theautomated ground control is configured to operate outside of air trafficcontrol purview.
 19. The method of claim 11, wherein the remote deviceis configured to be located at the location of the first location datum.20. The method of claim 11, wherein the authority status is configuredto be modified by a user.